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01_OneHotEncoding.ipynb		01_OneHotEncoding.ipynb
02_Bag_of_Words.ipynb		02_Bag_of_Words.ipynb
03_Bag_of_N_Grams.ipynb		03_Bag_of_N_Grams.ipynb
04_TF_IDF.ipynb		04_TF_IDF.ipynb
05_Pre_Trained_Word_Embeddings.ipynb		05_Pre_Trained_Word_Embeddings.ipynb
06_Training_embeddings_using_gensim.ipynb		06_Training_embeddings_using_gensim.ipynb
07_DocVectors_using_averaging_Via_spacy.ipynb		07_DocVectors_using_averaging_Via_spacy.ipynb
08_Training_Dov2Vec_using_Gensim.ipynb		08_Training_Dov2Vec_using_Gensim.ipynb
09_Visualizing_Embeddings_Using_TSNE.ipynb		09_Visualizing_Embeddings_Using_TSNE.ipynb
10_Visualizing_Embeddings_using_Tensorboard.ipynb		10_Visualizing_Embeddings_using_Tensorboard.ipynb
README.md		README.md
ch3-requirements.txt		ch3-requirements.txt

README.md

Text Representation

To be added

Set of notebooks associated with the chapter.

One-Hot Encoding: Here we demonstrate One-Hot encoding from the first principle as well as scikit learn's implementation on our toy corpus.
Bag of Words : Here we demonstrate how to arrive at the bag of words representation for our toy corpus.
Bag of N Grams: Here we demonstrate how Bag of N-Grams work using our toy corpus.
TF-IDF: Here we demonstrate how to obtain the get the TF-IDF representation of a document using sklearn's TfidfVectorizer(we will be using our toy corpus).
Pre-trained Word Embeddings: Here we demonstrate how we can represent text using pre-trained word embedding models and how to use them to get representations for the full text.
Custom Word Embeddings: Here we demonstrate how to train a custom Word Embedding model(word2vec) using gensim on both, our toy corpus and a subset of Wikipedia data.
Vector Representations via averaging: Here we demonstrate averaging of Document Vectors using spaCy.
Doc2Vec Model: Here we demonstrate how to train your own doc2vec model.
Visualizing Embeddings Using TSNE: Here we demonstrate how we can use dimensionality reduction techniques such as TSNE to visualize embeddings.
Visualizing Embeddings using Tensorboard: Here we demonstrate how we can visualize embeddings using Tensorboard.

Color figures as requested by the readers.